Contextual the technological factors that impact the performance of small and medium enterprises (SMEs) in UAE
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The study relied on analyzing the relationship between several factors that represent technological factors (Technology orientation, Utilizing new knowledge Technology Adoption, Knowledge compatibility, Technological capabilities and Technological collaboration) as independent factors and the level of performance as a dependent factor in medium and small-sized companies in the UAE economy. The inferential approach was followed in the study by analyzing the significance of the sampling results that were reached using structural equations based on the Partial Least Squares method (Pls) using the Smart Plus package, which was used to analyze the path between the six factors as independent factors and the performance factor as a dependent factor. The study applied this methodology to a sample of 250 individuals working in medium and small-sized companies in the UAE economy. The study concluded that the most closely related to performance was Technological Collaboration with an effect size of 72.3%. However, the least related factor was Technology orientation, with an effect factor of 34.4%, while the correlation of the other four factors with performance was medium (between 50% and 63%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it